genome music path-scan

NAME

genome music path-scan - Find significantly mutated pathways in a cohort given a list of somatic mutations.

VERSION

This document describes genome music path-scan version 0.04 (2018-07-05 at 09:17:13)

SYNOPSIS

genome music path-scan --gene-covg-dir=? --bam-list=? --pathway-file=? --maf-file=? --output-file=? [--bmr=?] [--genes-to-ignore=?] [--min-mut-genes-per-path=?] [--skip-non-coding] [--skip-silent]

 ... music path-scan \
        --bam-list input_dir/bam_file_list \
        --gene-covg-dir output_dir/gene_covgs/ \
        --maf-file input_dir/myMAF.tsv \
        --output-file output_dir/sm_pathways \
        --pathway-file input_dir/pathway_dbs/KEGG.txt \
        --bmr 8.7E-07

REQUIRED ARGUMENTS

gene-covg-dir Text: Directory containing per-gene coverage files (Created using music bmr calc-covg)
bam-list Text: Tab delimited list of BAM files [sample_name, normal_bam, tumor_bam] (See Description)
pathway-file Text: Tab-delimited file of pathway information (See Description)
maf-file Text: List of mutations using TCGA MAF specifications v2.3
output-file Text: Output file that will list the significant pathways and their p-values

OPTIONAL ARGUMENTS

bmr Number: Background mutation rate in the targeted regions
Default value '1e-06' if not specified
genes-to-ignore Text: Comma-delimited list of genes whose mutations should be ignored
min-mut-genes-per-path Number: Pathways with fewer mutated genes than this, will be ignored
Default value '1' if not specified
skip-non-coding Boolean: Skip non-coding mutations from the provided MAF file
Default value 'true' if not specified
noskip-non-coding Boolean: Make skip-non-coding 'false'
skip-silent Boolean: Skip silent mutations from the provided MAF file
Default value 'true' if not specified
noskip-silent Boolean: Make skip-silent 'false'

DESCRIPTION

Only the following four columns in the MAF are used. All other columns may be left blank.

 Col 1: Hugo_Symbol (Need not be HUGO, but must match gene names used in the pathway file)
 Col 2: Entrez_Gene_Id (Matching Entrez ID trump gene name matches between pathway file and MAF)
 Col 9: Variant_Classification
 Col 16: Tumor_Sample_Barcode (Must match the name in sample-list, or contain it as a substring)

The Entrez_Gene_Id can also be left blank (or set to 0), but it is highly recommended, in case genes are named differently in the pathway file and the MAF file.

ARGUMENTS

--pathway-file

This is a tab-delimited file prepared from a pathway database (such as KEGG), with the columns: [path_id, path_name, class, gene_line, diseases, drugs, description] The latter three columns are optional (but are available on KEGG). The gene_line contains the "entrez_id:gene_name" of all genes involved in this pathway, each separated by a "|" symbol.

For example, a line in the pathway-file would look like:

  hsa00061    Fatty acid biosynthesis    Lipid Metabolism    31:ACACA|32:ACACB|27349:MCAT|2194:FASN|54995:OXSM|55301:OLAH

Ensure that the gene names and entrez IDs used match those used in the MAF file. Entrez IDs are not mandatory (use a 0 if Entrez ID unknown). But if a gene name in the MAF does not match any gene name in this file, the entrez IDs are used to find a match (unless it's a 0).

--gene-covg-dir

This is usually the gene_covgs subdirectory created when you run "music bmr calc-covg". It should contain files for each sample that report per-gene covered base counts.

--bam-list

Provide a file containing sample names and normal/tumor BAM locations for each. Use the tab- delimited format [sample_name normal_bam tumor_bam] per line. This tool only needs sample_name, so all other columns can be skipped. The sample_name must be the same as the tumor sample names used in the MAF file (16th column, with the header Tumor_Sample_Barcode).

--bmr

The overall background mutation rate. This can be calculated using "music bmr calc-bmr".

--genes-to-ignore

A comma-delimited list of genes to ignore from the MAF file. This is useful when there are recurrently mutated genes like TP53 which might mask the significance of other genes.

AUTHORS

 Michael Wendl, Ph.D.

CREDITS

This module uses reformatted copies of data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database:

 * KEGG - http://www.genome.jp/kegg/

2018-07-05

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